Modifying extracted text with regular expressions

Tired of cleaning up your scraped lists manually? Removing that pesky “sqft” suffix from “1,200 sqft” each time you create a list of properties in a spreadsheet? Now, you can configure the scraper to skip it entirely and extract only the data that matters. We’re introducing support for regular expressions in the scraper - an incredibly powerful way to fine-tune and adjust the format of exported text to fit your needs.

To apply RegEx formatting to the extracted fields:

• Click the three-dot menu of an extracted field in the bottom bar of the scraper;
• Select Edit;
• Expand the Lookup setup (advanced) section;
• Type in the RegEx that you want to apply to that field.

RegEx provides impressive capabilities when it comes to adjusting and formatting text, ranging from very simple to incredibly sophisticated. Some easy-to-use and popular RegEx patterns that you can apply are:

• To extract data from a full address: 4701 Amrock Dr., Johnstown, CO 80534some text
  • \d{5,}: Extracts the Zip address (80534)
  • .?, .?, ([A-Z]{2}): Extracts the State (CO)
  • (?<=,)(.+?)(?=,): Extracts the city (Johnstown)
  • ^(.*?)(?=,): Extracts the street address (4701 Amrock Dr.)

• To extract value that comes after a word:some text
  • (?<=From).+: Example: (From $500.000)

• To extract a number with delimiters:some text
  • [\d.,]|-: Extracts 3-5 from 3–5 Beds
  • [\d.,]|-: Extracts 2,447–3,779 from 2,447–3,779 sq. ft.

You can learn more about regular expressions here.